In image searching, users have a mental picture of content, such as images, desired to be retrieved. For example, a shopper wants to retrieve those catalog pages that match the shopper's envisioned style of clothing. In another example, a witness wants to help law enforcement locate a suspect in a database based on his/her memory of the face of the suspect. In a further example, a web page designer wants to find a stock photograph suitable for his/her customer's brand image. Oftentimes, such images are attempted to be retrieved based on simple keyword searching. However, such content or images (e.g., illustrations, photographs, online products) are not easily identified and retrieved based on simple keyword searching. In a similar manner, in other domains, such as video, document, or music retrieval, it is difficult to accurately meet a user's search needs if relying on keyword search alone.
As a result, interactive search techniques have been developed to attempt to identify and retrieve the content envisioned by the user by allowing the user to iteratively refine the results retrieved by the system. The basic idea in such techniques is to show the user candidate results, obtain feedback, and adapt the system's relevance ranking function accordingly. However, existing image search methods provide only a narrow channel of feedback to the system. Typically, a user refines the retrieved images via binary feedback (“relevant” or “irrelevant”) on exemplary images provided to the user or else attempts to tune the system parameters, such as weights on a small set of low-level features (e.g., texture, color, edges, shape). The latter is clearly a burden for a user who likely cannot understand the inner workings of the algorithm. The former feedback is more natural to supply, yet it leaves the system to infer what about those images the user found relevant or irrelevant, and therefore can be slow to converge on the user's envisioned content in practice. In a similar manner, binary relevance feedback on videos, audio files, documents, or other database items can be insufficient to accurately convey the user's search needs in those other domains.